Nurse‐assessed metabolic monitoring: A file audit of risk factor prevalence and impact of an intervention to enhance measurement of waist circumference
Bibliographic record
Abstract
The aim of the present study was to: (i) document the prevalence of risk factors for non-communicable diseases among mental health consumers (inpatients) with various diagnoses; and (ii) audit the frequency of waist circumference (WC) documentation before and after an intervention that involved a single nurse-education session, and change in assessment-form design. The study was undertaken in a private psychiatric hospital in Sydney, Australia. Twenty-five nurses participated in the educational intervention. File audits were performed prior to intervention delivery (n = 60), and 3 months' (n = 60), and 9 months' (n = 60) post-intervention. Files were randomly selected, and demographic (age, diagnosis) and risk factor (WC, body mass index (BMI), smoking status, blood pressure) data were extracted. WC was higher in this cohort compared to published general population means, and only 19% of patients had a BMI within the healthy range. In total, 37% of patients smoked, while 31% were hypertensive. At baseline, none of the audited files reported WC, which increased to 35 of the 60 (58%) files audited at the 3-month follow up. At the 9-month follow up, 25 of the 60 (42%) files audited reported a WC. In the 120 post-intervention files audited, only two patients refused measurement. These results illustrate the poor physical health of inpatients, and suggest that nurse-assessed metabolic monitoring can be enhanced with minimal training.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".